Advanced Lane Finding Project¶
The goals / steps of this project are the following:
- Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
- Apply a distortion correction to raw images.
- Use color transforms, gradients, etc., to create a thresholded binary image.
- Apply a perspective transform to rectify binary image ("birds-eye view").
- Detect lane pixels and fit to find the lane boundary.
- Determine the curvature of the lane and vehicle position with respect to center.
- Warp the detected lane boundaries back onto the original image.
- Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.
First, I'll compute the camera calibration using chessboard images¶
In [1]:
import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
%matplotlib inline
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((6*9,3), np.float32)
objp[:,:2] = np.mgrid[0:9, 0:6].T.reshape(-1,2)
In [2]:
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
# Make a list of calibration images
images = glob.glob('../camera_cal/calibration*.jpg')
img_list = []
img_d_list = []
# Step through the list and search for chessboard corners
for fname in images:
img = cv2.imread(fname)
img_list.append(img)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# Find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (9,6),None)
# If found, add object points, image points
if ret == True:
objpoints.append(objp)
imgpoints.append(corners)
# Draw and display the corners
img_d = cv2.drawChessboardCorners(img, (9,6), corners, ret)
img_d_list.append(img_d)
In [3]:
def calibrateImage(img, objpoints, imgpoints):
# Do camera calibration given object points and image points
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size,None,None)
dst = cv2.undistort(img, mtx, dist, None, mtx)
return dst, mtx, dist
def plotImage(img, undist, text1="", text2=""):
# 1x2 plots of color images
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
undist = cv2.cvtColor(undist, cv2.COLOR_BGR2RGB)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.imshow(img)
ax1.set_title(text1, fontsize=30)
ax2.imshow(undist)
ax2.set_title(text2, fontsize=30)
def plotImageBinary(img, undist, text1="", text2=""):
# 1x2 plots of grayscale images
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.imshow(img)
ax1.set_title(text1, fontsize=30)
ax2.imshow(undist, cmap='gray')
ax2.set_title(text2, fontsize=30)
def plotHistogram(hist, title='', xlabel='', ylabel=''):
# Histogram visualization
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(title)
plt.plot(hist)
In [4]:
# Test undistortion on an image
img = cv2.imread('../test_images/calibration1.jpg')
img_size = (img.shape[1], img.shape[0])
undist, mtx, dist = calibrateImage(img, objpoints, imgpoints)
plotImage(img, undist, 'Original Image', 'Calibrated Image')
Apply distortion correction to test images¶
In [5]:
# Test undistortion on an image
img = cv2.imread('../test_images/straight_lines1.jpg')
img_size = (img.shape[1], img.shape[0])
undist, mtx, dist = calibrateImage(img, objpoints, imgpoints)
#cv2.imwrite('../output_images/straight_lines1_undist.jpg',undist)
plotImage(img, undist, 'Original Image', 'Undistorted Image')
Compute object and image points for bird's eye-view (perspective transform)¶
In [6]:
def warpImage(img, src, dst):
img_size = (img.shape[1], img.shape[0])
M = cv2.getPerspectiveTransform(src, dst)
M_inv = cv2.getPerspectiveTransform(dst, src)
warped = cv2.warpPerspective(img, M, img_size, flags=cv2.INTER_LINEAR)
return warped, M, M_inv
In [7]:
color = [255, 0, 0]
thickness = 5
ud_cp = np.copy(undist)
#Left Line
x1 = 560
y1 = 460
x2 = 180
y2 = 710
ud_cp = cv2.line(ud_cp, (x1, y1), (x2, y2), color, thickness)
src1 = np.float32([[x1, y1], [x2, y2]])
#Right Line
x1 = 720
y1 = 460
x2 = 1130
y2 = 710
ud_cp = cv2.line(ud_cp, (x1, y1), (x2, y2), color, thickness)
src2 = np.float32([[x2, y2], [x1, y1]])
#Top Line
x1 = 560
y1 = 460
x2 = 720
y2 = 460
ud_cp = cv2.line(ud_cp, (x1, y1), (x2, y2), color, thickness)
#Bot Line
x1 = 180
y1 = 710
x2 = 1130
y2 = 710
ud_cp = cv2.line(ud_cp, (x1, y1), (x2, y2), color, thickness)
plotImage(img, ud_cp, 'Full Image', 'ROI region')
In [8]:
offset = 100
dst = np.float32([[offset, offset], [offset, img_size[1]-offset],
[img_size[0]-offset, img_size[1]-offset],
[img_size[0]-offset, offset]])
src = np.vstack((src1, src2))
print('Source Points')
print(src)
print('Destination Points')
print(dst)
In [9]:
warped, M, M_inv = warpImage(undist, src, dst)
plotImage(ud_cp, warped, 'Normal View', 'Birds Eye View')
In [10]:
def abs_sobel_thresh(img, orient='x', sobel_kernel =3, thresh=(0, 255)):
# Apply the following steps to img
# 1) Convert to grayscale
# 2) Take the derivative in x or y given orient = 'x' or 'y'
# 3) Take the absolute value of the derivative or gradient
# 4) Scale to 8-bit (0 - 255) then convert to type = np.uint8
# 5) Create a mask of 1's where the scaled gradient magnitude
# is > thresh_min and < thresh_max
# 6) Return this mask as your binary_output image
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
if orient == 'x':
sobelv = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
elif orient == 'y':
sobelv = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
else:
print("Invalid orientation!")
abs_sobelv = np.absolute(sobelv)
scaled_sobel = np.uint8(255*abs_sobelv/np.max(abs_sobelv))
sxbinary = np.zeros_like(scaled_sobel)
sxbinary[(scaled_sobel >= thresh[0]) & (scaled_sobel <= thresh[1])] = 1
return sxbinary
def mag_thresh(img, sobel_kernel=3, mag_thresh=(0, 255)):
# Apply the following steps to img
# 1) Convert to grayscale
# 2) Take the gradient in x and y separately
# 3) Calculate the magnitude
# 4) Scale to 8-bit (0 - 255) and convert to type = np.uint8
# 5) Create a binary mask where mag thresholds are met
# 6) Return this mask as your binary_output image
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
abs_sobel = np.sqrt(sobelx**2 + sobely**2)
scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
thresh_min = mag_thresh[0]
thresh_max = mag_thresh[1]
sxbinary = np.zeros_like(scaled_sobel)
sxbinary[(scaled_sobel >= thresh_min) & (scaled_sobel <= thresh_max)] = 1
return sxbinary
def dir_threshold(img, sobel_kernel=3, thresh=(0, np.pi/2)):
# Apply the following steps to img
# 1) Convert to grayscale
# 2) Take the gradient in x and y separately
# 3) Take the absolute value of the x and y gradients
# 4) Use np.arctan2(abs_sobely, abs_sobelx) to calculate the direction of the gradient
# 5) Create a binary mask where direction thresholds are met
# 6) Return this mask as your binary_output image
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
abs_sobelx = np.absolute(sobelx)
abs_sobely = np.absolute(sobely)
grad_dir = np.arctan2(abs_sobely, abs_sobelx)
thresh_min = thresh[0]
thresh_max = thresh[1]
sxbinary = np.zeros_like(grad_dir)
sxbinary[(grad_dir >= thresh_min) & (grad_dir <= thresh_max)] = 1
return sxbinary
# Define a function that thresholds the S-channel of HLS
# Use exclusive lower bound (>) and inclusive upper (<=)
def hls_select(hls, thresh=(0, 255)):
# 1) Convert to HLS color space
# 2) Apply a threshold to the S channel
# 3) Return a binary image of threshold result
S = hls[:,:,2]
binary = np.zeros_like(S)
binary[(S > thresh[0]) & (S <= thresh[1])] = 1
return binary
In [11]:
# Choose a Sobel kernel size
def getBinaryImage(img):
ksize = 5 # Choose a larger odd number to smooth gradient measurements
# Apply each of the thresholding functions
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
hls_binary = hls_select(hls, thresh=(150, 255))
gradx = abs_sobel_thresh(img, orient='x', sobel_kernel=ksize, thresh=(20, 150))
grady = abs_sobel_thresh(img, orient='y', sobel_kernel=ksize, thresh=(20, 150))
mag_binary = mag_thresh(img, sobel_kernel=ksize, mag_thresh=(20, 150))
dir_binary = dir_threshold(img, sobel_kernel=ksize, thresh=(0.3, 1.3))
combined = np.zeros_like(dir_binary)
combined[((gradx == 1) & (grady == 1)) | ((mag_binary == 1) & (dir_binary == 1)) | (hls_binary == 1)] = 1
return combined
In [12]:
binary_warped = getBinaryImage(warped)
plotImageBinary(warped, binary_warped, 'Color Image', 'Binary Image')
In [13]:
histogram = np.sum(binary_warped[int(binary_warped.shape[0]/2):,:], axis=0)
plotHistogram(histogram, 'Lane points distribution', 'x-position', 'lane points')
Fine left and right lanes and track them¶
In [14]:
def fitLine(binary_warped, left_fit, right_fit):
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
return left_fitx, right_fitx
In [15]:
def checkWithPrev(binary_warped, prev_fit, margin = 100):
# Assume you now have a new warped binary image
# from the next frame of video (also called "binary_warped")
# It's now much easier to find line pixels!
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
ref_line = prev_fit[0]*(nonzeroy**2) + prev_fit[1]*nonzeroy + prev_fit[2]
#print('ref_line', ref_line)
lane_inds = ((nonzerox > (ref_line - margin)) & (nonzerox < (ref_line + margin)))
# Again, extract left and right line pixel positions
lane_x = nonzerox[lane_inds]
lane_y = nonzeroy[lane_inds]
# Fit a second order polynomial to each
new_fit = np.polyfit(lane_y, lane_x, 2)
return new_fit
In [16]:
def getNewBase(binary_warped):
# Assuming you have created a warped binary image called "binary_warped"
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[int(binary_warped.shape[0]/2):,:], axis=0)
# Create an output image to draw on and visualize the result
#out_img = np.uint8(np.dstack((binary_warped, binary_warped, binary_warped))*255)
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]/2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
return leftx_base, rightx_base
In [17]:
def searchNew(binary_warped, x_base, margin=100):
# Choose the number of sliding windows
nwindows = 9
# Set height of windows
window_height = np.int(binary_warped.shape[0]/nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated for each window
x_current = x_base
# Set minimum number of pixels found to recenter window
minpix = 50
# Create empty lists to receive left and right lane pixel indices
lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_x_low = x_current - margin
win_x_high = x_current + margin
# Draw the windows on the visualization image
#cv2.rectangle(out_img,(win_x_low,win_y_low),(win_x_high,win_y_high),(0,255,0), 2)
# Identify the nonzero pixels in x and y within the window
good_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_x_low) & (nonzerox < win_x_high)).nonzero()[0]
# Append these indices to the lists
lane_inds.append(good_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_inds) > minpix:
x_current = np.int(np.mean(nonzerox[good_inds]))
# Concatenate the arrays of indices
lane_inds = np.concatenate(lane_inds)
# Extract left and right line pixel positions
lane_x = nonzerox[lane_inds]
lane_y = nonzeroy[lane_inds]
# Fit a second order polynomial to each
new_fit = np.polyfit(lane_y, lane_x, 2)
return new_fit
In [18]:
leftx_base, rightx_base = getNewBase(binary_warped)
#Search left line
left_fit = searchNew(binary_warped, leftx_base)
#Search right line
right_fit = searchNew(binary_warped, rightx_base)
left_fitx, right_fitx = fitLine(binary_warped, left_fit, right_fit)
#plt.imshow(out_img)
In [19]:
def reprojectLane(binary_warped, undist, left_fitx, right_fitx, M_inv):
# Create an image to draw the lines on
warp_zero = np.zeros_like(binary_warped).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
# Recast the x and y points into usable format for cv2.fillPoly()
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
left_pt = pts_left[0,binary_warped.shape[0]-1]
right_pt = pts_right[0,0]
#print('left_pt', left_pt[0])
#print('right_pt', right_pt[0])
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, M_inv, (undist.shape[1], undist.shape[0]))
# Combine the result with the original image
result = cv2.addWeighted(undist, 1, newwarp, 0.3, 0)
result = cv2.cvtColor(result, cv2.COLOR_BGR2RGB)
return result, left_pt[0], right_pt[0]
In [20]:
result , left_pt, right_pt = reprojectLane(binary_warped, undist, left_fitx, right_fitx, M_inv)
plt.imshow(result)
Out[20]:
Track detected and new lines, Radius of curvature and Drift from center¶
In [21]:
# Define a class to receive the characteristics of each line detection
class Line():
def __init__(self):
# was the line detected in the last iteration?
self.detected = False
# x values of the last n fits of the line
self.recent_xfitted = []
#average x values of the fitted line over the last n iterations
self.bestx = None
#polynomial coefficients averaged over the last n iterations
self.best_fit = None
#polynomial coefficients for the most recent fit
self.current_fit = np.empty((0, 3), dtype='float')
#radius of curvature of the line in some units
self.radius_of_curvature = None
#distance in meters of vehicle center from the line
self.line_base_pos = None
#difference in fit coefficients between last and new fits
self.diffs = np.array([0,0,0], dtype='float')
#x values for detected line pixels
self.allx = None
#y values for detected line pixels
self.ally = None
#Average window size
self.win_size = 0
In [22]:
def radiusOfCurvature(binary_warped, undist, leftx, rightx, left_pt, right_pt):
# Define conversions in x and y from pixels space to meters
ym_per_pix = 30/binary_warped.shape[0] # meters per pixel in y dimension
xm_per_pix = 3.7/(right_pt - left_pt) # meters per pixel in x dimension
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
y_eval = np.max(ploty)
# Fit new polynomials to x,y in world space
left_fit_cr = np.polyfit(ploty*ym_per_pix, leftx*xm_per_pix, 2)
right_fit_cr = np.polyfit(ploty*ym_per_pix, rightx*xm_per_pix, 2)
# Calculate the new radii of curvature
left_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
right_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
# Now our radius of curvature is in meters
#print(left_curverad, 'm', right_curverad, 'm')
roc = np.mean(np.array([left_curverad, right_curverad]))
cal_center = np.mean(np.array([left_pt, right_pt]))
drift = ((binary_warped.shape[1] / 2) - cal_center) * xm_per_pix
return roc, drift
In [23]:
def lane_detect(img, mtx, dist, src, dst, left_line, right_line):
undist = cv2.undistort(img, mtx, dist, None, mtx)
warped, M, M_inv = warpImage(undist, src, dst)
binary_warped = getBinaryImage(warped)
leftx_base, rightx_base = getNewBase(binary_warped)
#Search left line
if(left_line.detected == False):
left_fit = searchNew(binary_warped, leftx_base)
left_line.detected = True
else:
left_fit = checkWithPrev(binary_warped, left_line.best_fit)
#Search right line
if(right_line.detected == False):
right_fit = searchNew(binary_warped, rightx_base)
right_line.detected = True
else:
right_fit = checkWithPrev(binary_warped, right_line.best_fit)
if(left_line.win_size < 5):
left_line.current_fit = np.append(left_line.current_fit, np.array([left_fit]), axis=0)
left_line.win_size += 1
else:
left_line.current_fit = np.delete(left_line.current_fit, (0), axis=0)
left_line.current_fit = np.append(left_line.current_fit, np.array([left_fit]), axis=0)
if(right_line.win_size < 5):
right_line.current_fit = np.append(right_line.current_fit, np.array([right_fit]), axis=0)
right_line.win_size += 1
else:
right_line.current_fit = np.delete(right_line.current_fit, (0), axis=0)
right_line.current_fit = np.append(right_line.current_fit, np.array([right_fit]), axis=0)
left_line.best_fit = np.mean(left_line.current_fit, axis=0)
right_line.best_fit = np.mean(right_line.current_fit, axis=0)
left_fitx, right_fitx = fitLine(binary_warped, left_line.best_fit, right_line.best_fit)
result, left_pt, right_pt = reprojectLane(binary_warped, undist, left_fitx, right_fitx, M_inv)
roc, drift = radiusOfCurvature(binary_warped, result, left_fitx, right_fitx, left_pt, right_pt)
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(result,'RoC=' + str(int(roc)) + 'm',(100,100), font, 2,(255,255,255),2,cv2.LINE_AA)
cv2.putText(result,'Drift=' + ("%.2f" % drift) + 'm',(100,200), font, 2,(255,255,255),2,cv2.LINE_AA)
result = cv2.cvtColor(result, cv2.COLOR_BGR2RGB)
return result
Read Video, detect and track lanes, compute ROC, Drift, write output¶
In [24]:
import sys
import skvideo.io as skio
inp = "project_video.mp4"
print('Reading video...')
inp_vid = skio.vread(inp)
print('Done')
In [25]:
print('Running Lane Detect...')
left_line = Line()
right_line = Line()
result = []
for i in range(len(inp_vid)):
out = lane_detect(inp_vid[i], mtx, dist, src, dst, left_line, right_line)
result.append(out)
print('Done')
result_arr = np.array(result)
plt.imshow(result_arr[0])
Out[25]:
In [26]:
out = "project_video_out.mp4"
print('Writing output video...')
skio.vwrite(out, result_arr)
print('Done')